AINeutralarXiv – CS AI · 8h ago7/10
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R-APS: Compositional Reasoning and In-Context Meta-Learning for Constrained Design via Reflective Adversarial Pareto Search
Researchers introduce R-APS (Reflective Adversarial Pareto Search), a novel method that enhances large language model reasoning for constrained design tasks by decomposing reasoning modes into separate contexts and orchestrating them across multiple timescales. The approach delivers 3.5x tighter robustness guarantees and 46% faster convergence on mechanical design problems without requiring model fine-tuning.